Algebraic theories for modeling components and their interactions offer abstraction over the specifics of component states and interfaces. For example, such theories deal with forms of sequential composition of two components in a manner independent of the type of data stored in the states of the components, and independent of the number and types of methods offered by the interfaces of the combinators. General purpose programming languages do not offer this level of abstraction, which implies that a gap must be bridged when turning component models into implementations. In this paper, we present an approach to prototyping of component-based systems that employs so-called type-level programming (or compile-time computation) to bridge the gap between abstract component models and their type-safe implementation in a functional programming language. We demonstrate our approach using Barbosa's model of components as generalized Mealy machines. For this model, we develop a combinator library in Haskell, which uses type-level programming with two effects. Firstly, wiring between components is computed during compilation. Secondly, the well-formedness of the component compositions is guarded by Haskell's strong type system.

Spreadsheets can be viewed as a highly flexible end-users programming environment which enjoys wide-spread adoption. But spreadsheets lack many of the structured programming concepts of regular programming paradigms. In particular, the lack of data structures in spreadsheets may lead spreadsheet users to cause redundancy, loss, or corruption of data during edit actions. In this paper, we demonstrate how implicit structural properties of spreadsheet data can be exploited to offer edit assistance to spreadsheet users. Our approach is based on the discovery of functional dependencies among data items which allow automatic reconstruction of a relational database schema. From this schema, new formulas and visual objects are embedded into the spreadsheet to offer features for auto-completion, guarded deletion, and controlled insertion. Schema discovery and spreadsheet enhancement are carried out automatically in the background and do not disturb normal user experience.

This paper presents techniques and tools to transform spreadsheets into relational databases and back. A set of data refinement rules is introduced to map a tabular datatype into a relational database schema. Having expressed the transformation of the two data models as data refinements, we obtain for free the functions that migrate the data. We use well-known relational database techniques to optimize and query the data. Because data refinements define bidirectional transformations we can map such database back to an optimized spreadsheet. We have implemented the data refinement rules and we have constructed tools to manipulate, optimize and refactor Excel-like spreadsheets.

Many errors in spreadsheet formulas can be avoided if spreadsheets are built automatically from higher-level models that can encode and enforce consistency constraints. However, designing such models is time consuming and requires expertise beyond the knowledge to work with spreadsheets. Legacy spreadsheets pose a particular challenge to the approach of controlling spreadsheet evolution through higher-level models, because the need for a model might be overshadowed by two problems: (A) The benefit of creating a spreadsheet is lacking since the legacy spreadsheet already exists, and (B) existing data must be transferred into the new model-generated spreadsheet. To address these problems and to support the model-driven spreadsheet engineering approach, we have developed a tool that can automatically infer ClassSheet models from spreadsheets. To this end, we have adapted a method to infer entity/relationship models from relational database to the spreadsheets/ClassSheets realm. We have implemented our techniques in the HAEXCEL framework and integrated it with the ViTSL/Gencel spreadsheet generator, which allows the automatic generation of refactored spreadsheets from the inferred ClassSheet model. The resulting spreadsheet guides further changes and provably safeguards the spreadsheet against a large class of formula errors. The developed tool is a significant contribution to spreadsheet (reverse) engineering, because it fills an important gap and allows a promising design method (ClassSheets) to be applied to a huge collection of legacy spreadsheets with minimal effort.

Spreadsheets are widely used by end users, and studies have shown that most end-user spreadsheets contain non-trivial errors. To improve end users productivity, recent research proposes the use of a model-driven engineering approach to spreadsheets. In this paper we conduct the first systematic empirical study to assess the effectiveness and efficiency of this approach. A set of spreadsheet end users worked with two different model-based spreadsheets, and we present and analyze the results achieved.

Spreadsheets are notoriously error-prone. To help avoid the introduction of errors when changing spreadsheets, models that capture the structure and inter-dependencies of spreadsheets at a conceptual level have been proposed. Thus, spreadsheet evolution can be made safe within the confines of a model. As in any other model/instance setting, evolution may not only require changes at the instance level but also at the model level. When model changes are required, the safety of instance evolution can not be guarded by the model alone. Coupled transformation of models and instances are supported by the 2LT platform and have been applied for transformation of algebraic datatypes, XML schemas, and relational database models. We have extended 2LT to spreadsheet evolution. We have designed an appropriate representation of spreadsheet models, including the fundamental notions of formulæ, references, and blocks of cells. For these models and their instances, we have designed coupled transformation rules that cover specific spreadsheet evolution steps, such as extraction of a block of cells into a separate sheet or insertion of columns in all occurrences of a repeated block of cells. Each model-level transformation rule is coupled with instance level migration rules from the source to the target model and vice versa. These coupled rules can be composed to create compound transformations at the model level that induce compound transformations at the instance level. With this approach, spreadsheet evolution can be made safe, even when model changes are involved.

This paper describes the embedding of ClassSheet models in spreadsheet systems. ClassSheet models are well-known and describe the business logic of spreadsheet data. We embed this domain specific model representation on the (general purpose) spreadsheet system it models. By defining such an embedding, we provide end users a model-driven engineering spreadsheet developing environment. End users can interact with both the model and the spreadsheet data in the same environment. Moreover, we use advanced techniques to evolve spreadsheets and models and to have them synchronized. In this paper we present our work on extending a widely used spreadsheet system with such a model-driven spreadsheet engineering environment.

Spreadsheets are notoriously error-prone. To help avoid the introduction of errors when changing spreadsheets, models that capture the structure and interdependencies of spreadsheets at a conceptual level have been proposed. Thus, spreadsheet evolution can be made safe within the confines of a model. As in any other model/instance setting, evolution may not only require changes at the instance level but also at the model level. When model changes are required, the safety of instance evolution can not be guarded by the model alone. We have designed an appropriate representation of spreadsheet models, including the fundamental notions of formulæand references. For these models and their instances, we have designed coupled transformation rules that cover specific spreadsheet evolution steps, such as the insertion of columns in all occurrences of a repeated block of cells. Each model-level transformation rule is coupled with instance level migration rules from the source to the target model and vice versa. These coupled rules can be composed to create compound transformations at the model level inducing compound transformations at the instance level. This approach guarantees safe evolution of spreadsheets even when models change.

Spreadsheets are widely used, and studies have shown that most end-user spreadsheets contain non-trivial errors. To improve end-users productivity, recent research proposes the use of a model-driven engineering approach to spreadsheets. In this paper we conduct the first systematic empirical study to assess the effectiveness and efficiency of this approach. A set of spreadsheet end users worked with two different model-based spreadsheets, and we present and analyze here the results achieved.

Spreadsheetsarewidelyusedandstudiesshowthatmostoftheexisting ones contain non-trivial errors. To improve end-users productivity, recent research proposes the use of a model-driven engineering approach to spreadsheets. In this paper we conduct the first empirical study to assess the effectiveness and efficiency of this approach. A set of spreadsheet end users worked with two different model-based spreadsheets. We present and analyze here the results achieved.

Spreadsheets can be viewed as programming languages for non-professional programmers. These so-called ``end-user'' programmers vastly outnumber professional programmers creating millions of new spreadsheets every year. As a programming language, spreadsheets lack support for abstraction, testing, encapsulation, or structured programming. As a result, and as numerous studies have shown, the high rate of production is accompanied by an alarming high rate of errors. Some studies report that up to 90% of real-world spreadsheets contain errors. After their initial creation, many spreadsheets turn out to be used for storing and processing increasing amounts of data and supporting increasing numbers of users over long periods of time, making them complicated systems. An emerging solution to handle the complex and evolving software systems is Model-driven Engineering (MDE). To consider models as first class entities and any software artifact as a model or a model element is one of the basic principles of MDE. We adopted some techniques from MDE to solve spreadsheet problems. Most spreadsheets (if not all) lack a proper specification or a model. Using reverse engineering techniques we are able to derive various models from legacy spreadsheets. We use functional dependencies (a formalism that allow us to define how some column values depend on other column values) as building blocks for these models. Models can be used for several spreadsheet improvements, namely refactoring, safe evolution, migration or even generation of edit assistance. The techniques presented in this work are available under the framework HAEXCEL that we developed. It is composed of online and batch tools, reusable HASKELL libraries and OpenOffice.org extensions. A study with several end-users was organized to survey the impact of the techniques we designed. The results of this study indicate that the models can bring great benefits to spreadsheet engineering helping users to commit less errors and to work faster.

n this extended abstract we present a bidirectional model-driven framework to develop spreadsheets. By being model driven, our approach allows to evolve a spreadsheet model and automatically have the data co-evolved. The bidirectional component achieves precisely the inverse, that is, to evolve the data and automatically obtain a new model to which the data conforms.

Spreadsheets play an important role in software organizations. Indeed, in large software organizations, spreadsheets are not only used to define sheets containing data and formulas, but also to collect information from different systems, to adapt data coming from one system to the format required by another, to perform operations to enrich or simplify data, etc. In fact, over time many spreadsheets turn out to be used for storing and processing increasing amounts of data and supporting increasing numbers of users. Unfortunately, spreadsheet systems provide poor support for modularity, abstraction, and transformation, thus, making the maintenance, update and evolution of spreadsheets a very complex and error-prone task. We present techniques for model-driven spreadsheet engineering where we employ bidirectional transformations to maintain spreadsheet models and instances synchronized. In our setting, the business logic of spreadsheets is defined by ClassSheet models to which the spreadsheet data conforms, and spreadsheet users may evolve both the model and the data instances. Our techniques are implemented as part of the MDSheet framework: an extension for a traditional spreadsheet system.

n this paper we explore the use of models in the context of spreadsheet engineering. We review a successful spreadsheet modeling language, whose semantics we further extend. With this extension we bring spreadsheet models closer to the business models of spreadsheets themselves. An addon for a widely used spreadsheet system, providing bidirectional model-driven spreadsheet development, was also improved to include the proposed model extension.

Spreadsheets are among the most popular programming languages in the world. Unfortunately, spreadsheet systems were not tailored from scratch with modern programming language features that guarantee, as much as possible, program correctness. As a consequence, spreadsheets are populated with unacceptable amounts of errors. In other programming language settings, model-based approaches have been proposed to increase productivity and program effectiveness. Within spreadsheets, this approach has also been followed, namely by ClassSheets. In this paper, we propose an extension to ClassSheets to allow the specification of spreadsheets that can be viewed as relational databases. Moreover, we present a transformation from ClassSheet models to UML class diagrams enriched with OCL constraints. This brings to the spreadsheet realm the entire paraphernalia of model validation techniques that are available for UML.

n this paper, we present MDSHEET, a framework for the embedding, evolution and inference of spreadsheet models. This framework offers a model-driven software development mechanism for spreadsheet users.

Although spreadsheets can be seen as a flexible programming environment, they lack some of the concepts of regular programming languages, such as structured data types. This can lead the user to edit the spreadsheet in a wrong way and perhaps cause corrupt or redundant data. We devised a method for extraction of a relational model from a spreadsheet and the subsequent embedding of the model back into the spreadsheet to create a model-based spreadsheet programming environment. The extraction algorithm is specific for spreadsheets since it considers particularities such as layout and column arrangement. The extracted model is used to generate formulas and visual elements that are then embedded in the spreadsheet helping the user to edit data in a correct way. We present preliminary experimental results from applying our approach to a sample of spreadsheets from the EUSES Spreadsheet Corpus.

Spreadsheets can be viewed as programming languages for non-professional programmers. These so-called ``end-user'' programmers vastly outnumber professional programmers creating millions of new spreadsheets every year. As a programming language, spreadsheets lack support for abstraction, testing, encapsulation, or structured programming. As a result, and as numerous studies have shown, the high rate of production is accompanied by an alarming high rate of errors. Some studies report that up to 90% of real-world spreadsheets contain errors. After their initial creation, many spreadsheets turn out to be used for storing and processing increasing amounts of data and supporting increasing numbers of users over long periods of time, making them complicated systems. An emerging solution to handle the complex and evolving software systems is Model-driven Engineering (MDE). To consider models as first class entities and any software artifact as a model or a model element is one of the basic principles of MDE. We adopted some techniques from MDE to solve spreadsheet problems. Most spreadsheets (if not all) lack a proper specification or a model. Using reverse engineering techniques we are able to derive various models from legacy spreadsheets. We use functional dependencies (a formalism that allow us to define how some column values depend on other column values) as building blocks for these models. Models can be used for several spreadsheet improvements, namely refactoring, safe evolution, migration or even generation of edit assistance. The techniques presented in this work are available under the framework HAEXCEL that we developed. It is composed of online and batch tools, reusable HASKELL libraries and OpenOffice.org extensions. A study with several end-users was organized to survey the impact of the techniques we designed. The results of this study indicate that the models can bring great benefits to spreadsheet engineering helping users to commit fewer errors and to work faster.

In this paper we present a quality model for spreadsheets, based on the ISO/IEC 9126 standard that defines a generic quality model for software. To each of the software characteristics defined in the ISO/IEC 9126, we associate an equivalent spreadsheet characteristic. Then, we propose a set of spreadsheet specific metrics to assess the quality of a spreadsheet in each of the defined characteristics. In order to obtain the normal distribution of expected values for a spreadsheet in each of the metrics that we propose, we have executed them against all spreadsheets in the large and widely used EUSES spreadsheet corpus. Then, we quantify each characteristic of our quality model after computing the values of our metrics, and we define quality scores for the different ranges of values. Finally, to automate the atribution of a quality score to a given spreadsheet, according to our quality model, we have integrated the computation of the metrics it includes in both a batch and a web-based tool.

This tool demo paper presents SmellSheet Detective: a tool for automatically detecting bad smells in spreadsheets. We have defined a catalog of bad smells in spreadsheet data which was fully implemented in a reusable library for the manipulation of spreadsheets. This library is the building block of the SmellSheet Detective tool, that has been used to detect smells in large, real-world spreadsheet within the EUSES corpus, in order to validate and evolve our bad smells catalog.

Spreadsheets are considered to be the most widely used programming language in the world, and reports have shown that 90% of real-world spreadsheets contain errors. In this work, we try to identify spreadsheet smells, a concept adapted from software, which consists of a surface indication that usually corresponds to a deeper problem. Our smells have been integrated in a tool, and were computed for a large spreadsheet repository. Finally, the analysis of the results we obtained led to the refinement of our initial catalog.

Spreadsheets are widely recognized as popular programming systems with a huge number of spreadsheets being created every day. Also, spreadsheets are often used in the decision processes of profit-oriented companies. While this illustrates their practical importance, studies have shown that up to 90% of real-world spreadsheets contain errors. In order to improve the productivity of spreadsheet end-users, the software engineering community has proposed to employ model-driven approaches to spreadsheet development. In this paper we describe the evaluation of a bidirectional model-driven spreadsheet environment. In this environment, models and data instances are kept in conformity, even after an update on any of these artifacts. We describe the issues of an empirical study we plan to conduct, based on our previous experience with end-user studies. Our goal is to assess if this model-driven spreadsheet development framework does in fact contribute to improve the productivity of spreadsheet users.

This paper proposes a set of metrics for the assessment of the complexity of models defining the business logic of spreadsheets. This set can be considered the first step in the direction of building a quality standard for spreadsheet models, that is still to be defined. The computation of concrete metric values has further been integrated under a well-established model-driven spreadsheet development environment, providing a framework for the analysis of spreadsheet models under spreadsheets themselves.

Spreadsheets are being used with many different purposes that range from toy applications to complete information systems. In any of these cases, they are often used as data repositories that can grow significantly. As the amount of data grows, it also becomes more difficult to extract concrete information out of them. This paper focuses on the problem of spreadsheet querying. In particular, we propose an expressive and composable technique where intuitive queries can be defined. Our approach builds on a model-driven spreadsheet development environment, and queries are expressed referencing entities in the model of a spreadsheet instead of in its actual data. Finally, the system that we have implemented relies on Google's query function for spreadsheets.